https://mikelayuso.com/papers/PapersHugoBlox Kit (https://hugoblox.com)en-usThu, 23 Apr 2026 00:00:00 +0000https://mikelayuso.com/media/icon_hu_b0970716f810afd6.pnghttps://mikelayuso.com/papers/https://mikelayuso.com/papers/lmd-process-monitoring/Wed, 16 Oct 2024 00:00:00 +0000https://mikelayuso.com/papers/lmd-process-monitoring/<h2 id="abstract">Abstract</h2> <p>Metal Additive Manufacturing (AM) allows producing geometrically complex metal components, unlocking new design possibilities and making it suitable to sectors such as healthcare, automotive and aerospace. AM processes are complex and require the use of many sensors to extract relevant process information for its monitoring and control. In the last years, many studies have applied advanced Deep Learning methods to extract knowledge from AM processes. However, these developments are specific to a particular setup, problem or defectology. Furthermore, they lack frameworks and pipelines to guide throughout their development, and do not include AI-related tools for data labelling, visualization, and AI model development and deployment. With the aim of simplifying the development and deployment of AM process monitoring systems, a dashboard-based framework that makes use of AI for anomaly detection and for feature extraction is presented in this study. The framework helps with development and deployment of monitoring systems by easing the incorporation of new sensors and the extraction of new features from captured data by end users. In this study, a Laser Metal Deposition (LMD) process is considered as the use case to show the usefulness of the developed framework.</p> <h2 id="key-contributions">Key Contributions</h2> <ul> <li>3D visualization framework for LMD process monitoring</li> <li>Sensor fusion techniques for comprehensive process data</li> <li>Machine learning approaches for anomaly detection</li> </ul>https://mikelayuso.com/papers/ai-aas-ids/Thu, 27 Jun 2024 00:00:00 +0000https://mikelayuso.com/papers/ai-aas-ids/<h2 id="abstract">Abstract</h2> <p>This article develops an architecture for the implementation of Artificial Intelligence in the manufacturing value chain based on standard technologies and data spaces. The standards considered are IEC 63278 “Asset Administration Shell (AAS) for industrial applications” and DIN SPEC 27070:2020 – “Requirements and reference architecture of a security gateway for the exchange of industry data and services“ by IDSA. The architecture provides a data space that allows MONDRAGON industrial cooperatives to use data for the execution of advanced data analytics, Artificial Intelligence (AI) algorithms and interoperability between assets and IoT-platforms. The development of knowledge in this field allows, on the one hand, to optimise the consolidation of data as a strategic factor and, on the other hand, to increase collaboration between manufacturing companies, suppliers and technology providers. The article also explores specific Artificial Intelligence technologies with a wide application in industrial environments. In particular, the study has focused on research into Low/No Code, Explainability (XAI) tools and incremental learning algorithms. The contributions of this paper are summarised in 1) creating an IDS-AAS based architecture and data space that allows the exploitation of AI use cases, either by directly downloading models or by using AI as a service, 2) identifying useful AI tools for industry such as AutoML, No/Low code, XAI or incremental learning, 3) implementing a use case where different AI use alternatives are implemented.</p> <h2 id="key-contributions">Key Contributions</h2> <ul> <li>Framework for AAS-based AI system integration</li> <li>Data sovereignty approaches using IDS principles</li> <li>Practical implementation guidelines for smart factories</li> </ul>https://mikelayuso.com/papers/hyperconnected-architecture/Tue, 21 Sep 2021 00:00:00 +0000https://mikelayuso.com/papers/hyperconnected-architecture/<h2 id="abstract">Abstract</h2> <p>The HyperCOG project addresses the full digital transformation of process industry through an innovative Industrial Cyber-Physical System and Data Analytics. It is based on advanced technologies that enable the development of a hyperconnected network of digital nodes. The nodes can catch outstanding streams of data in real-time, which together with the high computing capabilities, provide sensing, knowledge and cognitive reasoning, making companies robust in the face of variant scenarios. The breaking-edge system proposed in this work is validated on productivity, environmental and replicability aspects on three use cases of three different sectors: steel, cement and chemical.</p> <h2 id="key-contributions">Key Contributions</h2> <ul> <li>Architecture for seamless data integration across production systems</li> <li>Cognitive layer design for intelligent decision support</li> <li>Implementation guidelines for Industry 4.0 transition</li> </ul>https://mikelayuso.com/papers/cpps-industry-4-0/Mon, 23 Aug 2021 00:00:00 +0000https://mikelayuso.com/papers/cpps-industry-4-0/<h2 id="abstract">Abstract</h2> <p>The Hyperconnected Architecture for High Cognitive Production Plants (HyperCOG) project aims at the process industry’s complete digital transformation through an advanced Industrial Cyber-Physical Infrastructure. It is based on advanced technologies that allow a hyperconnected network of digital nodes to be created improving the classic automation hierarchy of communication layers. The nodes will collect data streams in real-time, offering cognitive sensing and information along with high performance computing capabilities making the process industry businesses solid in different scenarios. The system is validated in three fields of the process industry: steel, cement and chemical where optimization in the use of energy and raw materials is obtained, among other benefits.</p> <h2 id="key-contributions">Key Contributions</h2> <ul> <li>Framework for CPPS implementation in smart factories</li> <li>Integration patterns for legacy and modern manufacturing equipment</li> <li>Case studies demonstrating improved production efficiency</li> </ul>https://mikelayuso.com/papers/surface-crack-detection/Sun, 31 Dec 2017 00:00:00 +0000https://mikelayuso.com/papers/surface-crack-detection/<h2 id="abstract">Abstract</h2> <p>The aim of this work is to detect open surface cracks on metallic welds using laser thermography and posterior processing algorithms. In the last years the trend on welding processes has been to extend their use to applications with higher stress conditions. As a consequence, obtained welded regions are more prone to cracks. The types of cracks originated from welding processes are, in general, superficial and their detection is not straightforward since it detection is affected by their width. Traditional crack detection Non Destructive Techniques, such as Dye Penetrant or Magnetic Particles, are time consuming and require an experienced technician to correctly interpret the results. Furthermore, they foul the surface which needs to be cleaned after the test. Laser Thermography [5] [10] has several advantages over these traditional techniques: it is fast, non-contact and clean. In this paper, dynamic tests have been performed by scanning the weld maintaining the laser and camera motionless, and moving the piece linearly across them, so that the weld is scanned. The use of a scanning laser line to heat the surface allows the detection of the characteristic disruption that cracks generate in the natural diffusion of heat on the surface. Briefly, cracks block the heat front. This makes the temperature just before the crack to increase, while keeping the temperature of the region just after it at similar values for larger times than for other nearby regions. This thermal effect is known as “Thermal barrier” [11]. Here, a new method to detect the cracks based on this effect has been developed starting from two different approaches.</p> <h2 id="key-contributions">Key Contributions</h2> <ul> <li>Novel laser thermography setup optimized for surface crack detection</li> <li>Thermal Barrier effect modeling for improved signal processing</li> <li>Experimental validation on various material types</li> </ul>https://mikelayuso.com/papers/spot-welding-monitoring/Fri, 30 Jun 2017 00:00:00 +0000https://mikelayuso.com/papers/spot-welding-monitoring/<h2 id="abstract">Abstract</h2> <p>This work is a continuation of our previous work on the development of a monitoring system of a Spot Welding production line. Here we use the process information and photographs of more than 150,000 parts to improve the predictions of the previously developed fuzzy algorithm to predict the degradation state of the electrode. And, we present an alternative method based on deep-learning that aims at substituting the image analysis software developed by us to extract values associated with the quality level of the welded parts from photographs. The deep-learning algorithm learned here is applied to compress original photographs to a 15×15 pixels size image using an encoding / decoding model. Obtained compressed images are then used to predict quality parameters from a fuzzy rule-based classification algorithm. The results are promising and show that compressed images keep the relevant information from the original image that serve to directly determine the degree of the degradation of the electrode without requiring the use of previously developed image analysis software.</p> <h2 id="key-contributions">Key Contributions</h2> <ul> <li>Hybrid fuzzy-deep learning architecture for weld quality classification</li> <li>Real-time monitoring capabilities for production environments</li> <li>Comprehensive validation across different welding scenarios</li> </ul>https://mikelayuso.com/papers/electrode-degradation/Thu, 30 Jun 2016 00:00:00 +0000https://mikelayuso.com/papers/electrode-degradation/<h2 id="abstract">Abstract</h2> <p>This work presents the preliminary analysis done to determine the electrode degradation during a resistance spot welding manufacturing process of aluminium-based unions. This process represents a critical step in a high production rate manufacturing line, where currently identical welding parameters are used for the tens of thousands of dayly produced parts. During the welding process, the aluminium can melt locally, producing droplets that adhere to the electrode speeding its degradation that affects the quality of the joint. Degradation is hard to predict since it depends on part surface quality, electrode preparation and system set-up. In this work, an analysis of the degradation of the electrode associated with the observed and measured quality of the parts is carried out. With the final aim to advance toward a zero defect manufacturing scenario and increase the quality of the produced parts, a process data analysis based on fuzzy logic algorithms will be performed. An assessment of (i) produced part quality, (ii) the electrode degradation state, (iii) detection of different performance modes of the parameters and (iv) detection of events or process changes, will be done using image and production line data. This will allow to determine (i) when the electrode must be changed and (ii) the optimal combination of welding parameters to extend electrodes&rsquo; life assuring welding quality.</p> <h2 id="key-contributions">Key Contributions</h2> <ul> <li>Detailed characterization of electrode degradation patterns</li> <li>Analysis of process parameters affecting electrode lifespan</li> <li>Recommendations for electrode maintenance and replacement</li> </ul>